Skip What You Can Predict: Predictive Repositioning for Policy Optimization for Efficient LLM Training

Reinforcement learning with verifiable rewards (RLVR) can improve the reasoning ability of large language models, but repeatedly updating a policy on the same rollout batch is expensive: every additional update requires another backward pass, and multi-step methods pay for all intermediate optimizat